(1) Field of Invention
The present invention relates to a system for predicting significant events and, more particularly, to a system for predicting significant events using a progress curve model.
(2) Description of Related Art
Current forecasting methods for analysis of significant events can be primarily divided into four categories: 1) statistical modeling of time series; 2) machine learning methods; 3) analysis of influence of social behavior on economic markets (behavioral finance); and 4) empirical findings of patterns in time series behavior. The most popular data for financial prediction are time series of returns, stock indexes (i.e., closing/opening prices), volume of transactions, and interest rates.
Wang, Wang, Zhang and Guo (see the List of Incorporated Cited Literature References, Literature Reference No. 9) employed three methods for forecasting stock market price index: the exponential smoothing model (ESM), autoregressive integrated moving average model (ARIMA), and the back propagation neural network (BPNN). In their paper, a hybrid approach combining ESM, ARIMA, and BPNN is proposed to be the most advantageous of all three models. The weight of the proposed hybrid model (PHM) was determined by genetic algorithm (GA). The closing of the Shenzhen Integrated Index (SZII) and opening of the Dow Jones Industrial Average Index (DJIAI) were used as illustrative examples to evaluate the performances of the PHM. Numerical results showed that the proposed model outperforms all traditional models, including ESM, ARIMA, BPNN, the equal weight hybrid model (EWH), and the random walk model (RWM).
Additionally, Carpinteiro, Leite, Pinheiro, and Lima (see Literature Reference No. 2) compared three machine learning techniques for forecasting: multilayer perceptron, support vector machine, and hierarchical model. The hierarchical model is made up of a self-organizing map and a support vector machine, the latter on top of the former. The models are trained and assessed on a time series of a Brazilian stock market fund. The results from the experiments show that the performance of the hierarchical model is better than that of the support vector machine, and much better than that of the multilayer perceptron.
Further, Bollen, Mao and Zeng (see Literature Reference No. 1) demonstrated an approach of studying the influence of society mood states on economic markets, as opposed to behavioral finance, which focuses on the collective psychology of individuals involved in sale processes. In particular, they investigated whether measurements of collective mood states derived from large-scale Twitter feeds are correlated to the value of the Dow Jones Industrial Average (DJIA) over time. The text content of daily Twitter feeds was analyzed by two mood tracking tools, namely OpinionFinder which measures positive versus negative mood, and Google-Profile of Mood States (GPOMS) which measures mood in terms of six dimensions (Calm, Alert, Sure, Vital, Kind, and Happy). A Granger causality analysis and a Self-Organizing Fuzzy Neural Network were used to investigate the hypothesis that public mood states, as measured by the OpinionFinder and GPOMS mood time series, are predictive of changes in DJIA closing values. Obtained results indicated that the accuracy of DJIA predictions can be significantly improved by the inclusion of some public mood dimensions, but not others.
Moreover, Zantedeschi, Damien, and Poison (see Literature Reference No. 12) employed dynamic partition models to predict movements in the term structure of interest rates. This allowed the authors to investigate large historic cycles in the performance of how interest rates behave and to offer policy makers guidance regarding future expectations on their evolution. The authors used particle learning to learn about the unobserved state variables in a new class of dynamic product partition models that relate macro-variables to term structures. The empirical results, using data from 1970 to 2000, clearly identified some of the key shocks to the economy, such as recessions. Time series of Bayes factors served as a leading indicator of economic activity, validated via a Granger causality test.
In addition, Poison and Scott (see Literature Reference No. 11) proposed a model of a financial contagion that accounts for explosive, mutually exciting shocks to market volatility. The authors fit the model using country-level data during the European sovereign debt crisis, which has its roots in the period from 2008 to 2010, and continued to affect global markets up to October, 2011. Analysis presented in Literature Reference No. 11 showed that existing volatility models are unable to explain two key stylized features of global markets during presumptive contagion periods. The first feature was that shocks to aggregate market volatility can be sudden and explosive. The second feature was that shocks to aggregate market volatility are associated with specific directional biases in the cross-section of country-level returns. Their proposed model rectified this deficit by assuming that the random shocks to volatility are heavy-tailed and correlated cross-sectionally both with each other and with returns.
Furthermore, Preisa, Schneiderd, and Stanley (see Literature Reference No. 8) presented a novel approach resulting from studying patterns in transaction volumes, where fluctuations are characterized by abrupt switching creating upward and downward trends. They found scale-free behavior of the transaction volume after each switching. The universality of the results was tested by performing a parallel analysis of fluctuations in time intervals between transactions. The authors believed that their findings can be interpreted as being consistent with time-dependent collective behavior of financial market participants. Taking into account that fluctuations in financial markets can vary from hundreds of days to a few minutes, the authors raised the question of whether these ubiquitous switching processes have quantifiable features independent of the time horizon studied. Moreover, they suggested that the well-known catastrophic bubbles that occur on large time scales, such as the most recent financial crisis, may not be outliers but single dramatic representatives caused by the formation of increasing and decreasing trends on time scales varying over nine orders of magnitude from very large down to very small.
Finally, the prior art discloses a self-excited nultifractal statistical model describing changes of a particular time series rather than the time series themselves (see Literature Reference No. 13). Here, the authors proposed a model defined such that the amplitudes of the increments of the process were expressed as exponentials of a long memory of past increments. The principal feature of the model existed in the self-excitation mechanism combined with exponential nonlinearity (i.e., the explicit dependence of future values of the process on past ones). Distributions of daily changes of stock markets share the same features as distributions of the values of Z-scores: turbulent flows, seismicity of financial markets, multifractality, and heavy tailed probability density functions.
Each of the prior methods described above exhibit limitations that make them incomplete. Thus, a continuing need exists for an accurate prediction method for the prediction of significant events based on a progress curve model and its escalation parameters.